Wireless security cameras are closed-circuit television (CCTV) cameras that transmit a video and audio signal to a wireless receiver through a radio band. Many wireless security cameras require at least one cable or wire for power—the term “wireless” is sometimes used to refer only to the transmission process of video and/or audio. However, some wireless security cameras are battery-powered, making the cameras truly wireless from top to bottom.
Wireless cameras are proving very popular among modern security consumers due to their low installation costs and flexible mounting options. For example, there is no need to run expensive video extension cables, and wireless cameras can be mounted and/or installed in locations previously unavailable to standard wired cameras. In addition to the ease of use and convenience of access, wireless security cameras allow users to leverage broadband wireless internet to provide seamless video streaming over the internet.
Indoor tracking of people and objects is an area of critical importance for a wide variety of industries. Purely radio frequency (RF) or purely camera based (e.g., machine vision) tracking solutions have performance or corner case limitations that prevent them from becoming robust business intelligence tools.
For example, all existing methods of RF based indoor positioning have several limitations, ranging from large position inaccuracy (e.g., methods such as RF proximity, Received Signal Strength Indicator (RSSI) trilateration) to complex hardware architectures (e.g., RF triangulation, Time of Arrival (ToA), Time Difference of Arrival (TDoA)) to hefty processing requirements (e.g., RSSI fingerprinting). RSSI, or the Received Signal Strength Indicator, is a measure of the power level that a RF device, such as WiFi or 3G client, is receiving from the radio infrastructure at a given location and time. Other methods, such as RF multi angulation, use complex phased antenna arrays to determine both the RSSI and angle of arrival of an incoming RF signal. However, multiple radios dedicated to just this on a single device are needed in order to work. Moreover, RF Time of Arrival methods are cost prohibitive for anything that is shorter range than GPS because the hardware required to detect shorter flights is too expensive for commercial deployment.
Another method of increasing the accuracy of RF based indoor positioning is the use of RSSI fingerprinting to better model the RF surroundings. Traditionally this is done by placing a fixed beacon at a known distance from the access points and continuously monitoring the RSSI of its emissions. These are compared to the fixed Line of Sight approximated values to better model the access points surroundings. Modelling accuracy tends to increase with the total number of beacons deployed. However, deploying additional always-on beacons increases cost, and the total number of beacons rises at ⅓ the rate of the deployed access points for the least accurate method. Accordingly, in certain high deployment scenarios, one might not have the space to accommodate this.
Meanwhile, camera based indoor tracking solutions using computer vision/machine vision struggle with accuracy, even when using most advanced deep learning algorithms (trained by large datasets) and using very powerful hardware in the cloud. And for the instances in which all processing needs to be done on the device, there are even more constraints. There is a need to reduce computer vision processing requirements so that the processing requirements can fit within the camera's processing budget, but still offer people and object tracking benefits to users.